The early-stage design of a new microprocessor involves the evaluation of a wide range of benchmarks across a large number of architectural configurations. Several methods are used to cut down on the required simulation time. Typically, however, existing approaches fail to capture true program behaviour accurately and require a non-negligible number of training simulations to be run.
We address these problems by developing a machine learning model that predicts the mean of any given metric, e.g. cycles or energy, across a range of programs, for any microarchitectural configuration. It works by combining only the most representative programs from the benchmark suite based on their behaviour in the design space under consideration. We use our model to predict the mean performance, energy, energy-delay (ED) and energy-delay-squared (EDD) of the SPEC CPU 2000 and MiBench benchmark suites within our design space. We achieve the same level of accuracy as two state-of-the-art prediction techniques but require five times fewer training simulations. Furthermore, our technique is scalable and we show that, asymptotically, it requires an order of magnitude fewer simulations than these existing approaches.